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Valenyx Therapeutics · 2026

Redefining
nanobodies.

Pioneering multispecific nanobody-based biologics to conquer disease complexity and transform patient outcomes — starting with osimertinib-resistant non-small cell lung cancer.

01 · Platform

Discover. Design. Deliver.

An integrated platform that compresses the multispecific biologics workflow — from proteomic discovery, through epitope-first engineering, into an expanded set of clinical modalities.

01
Discover

Proteomics / ML engine

Serum-matured nanobody discovery from native disease proteomes, paired with ML-driven candidate triage — a 1012–1015 repertoire ranked for clinical developability before nomination.

  • Serum-matured nanobodies
  • 10¹²–10¹⁵ repertoire diversity
  • 1,000× library diversity
  • Pre-nomination developability ranking
02
Design

Epitope-first engineering

We pick the epitope before the molecule. Multi-paratopic constructs reach cryptic and low-abundance sites, with affinity tuned from fM through nM and humanization built in.

  • Affinity tuning · fM–pM–nM
  • Context modeling
  • Multi-specific architecture
  • Humanization
03
Deliver

Expanded modality

One platform, many modalities. Modular VHH formats compose into degrader-drug-conjugates, T-cell engagers, antibody-oligos, and brain-shuttle biologics — with manufacturing built for stability and yield.

  • Improved efficacy
  • Superior safety-tox profile
  • High production yield
  • Marked stability
1,000×
Library diversity vs. phage display
fM–pM
Binding affinities achieved
>90%
TGI · osimertinib-resistant CDX
50%
Less skin / heme tox vs. FDA mAb
Proteomics platform

From native biology to next-generation nanobodies.

Serum-matured repertoires sampled directly from the camelid, fractionated through high-resolution mass spectrometry, and ranked into developable multi-paratopic candidates.

01 · SOURCE
Native source
In-vivo affinity maturation across a 10¹²–10¹⁵ VHH repertoire — no synthetic library bias.
02 · ANALYSIS
Proteomics (MS)
High-resolution LC-MS/MS deconvolutes serum into single-clone sequences with quantitative abundance.
03 · OUTPUT
Nanobody discovery
ML-ranked multi-paratopic candidates with affinity tuning from fM through nM and humanization built in.
Native epitope accesscryptic · low-abundance
1,000× diversityvs. phage display
2× faster discoverypre-nomination ranking
Developable candidatesstability · yield
AI engine

Valenyx AI for ADC optimization.

Model. Compare. Optimize.

1
Epitope prediction
A
B

Native proteomes scored by an ML model — top-ranked epitopes surfaced before chemistry begins.

2
Format structure modeling
TAA-binding VHH
Internalization VHH
Fc scaffold
Payload

Candidate format, modeled at full structure with payload geometry.

3
Linker & domain order ranking
Linker size
(GS)3
(GS)4
(GS)5
Domain order
Order 1
Order 2
Order 3
Order N

Linker chemistry and domain arrangement scored head-to-head — winning configuration auto-selected.

4
Developability
Stability
Solubility
PK
Manufacturability
Immunogenicity

Top candidates passed through a five-axis developability filter — only manufacturable, low-risk molecules survive.

AI-Powered
Modeling
Multispecific
Design
Data-Driven
Ranking
Developable
Candidates
02 · Pipeline
Program Modality Indication Discovery Preclinical IND-enabling Phase 1 Status
VT-001 Tri-specific degrader-drug conjugate (DDC) NSCLC & solid tumors
IND-enabling
VT-002 Tri-specific dual-degrader-conjugate NSCLC & solid tumors
Preclinical
VT-003 Broad-tissue SdAb CNS & Muscle
Discovery
03 · Get in touch

Open to BD conversations, scientific collaborations, and exceptional talent across protein engineering, computational discovery, and translational oncology.

Thanks — we'll be in touch soon.